"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-aided human recognition – clustering
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Clothing genre classification by exploiting the style elements
Proceedings of the 20th ACM international conference on Multimedia
Efficient clothing retrieval with semantic-preserving visual phrases
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Apparel classification with style
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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Automatic clothes search in consumer photos is not a trivial problem as photos are usually taken under completely uncontrolled realistic imaging conditions. In this paper, a novel framework is presented to tackle this issue by leveraging low-level features (e.g., color) and high-level features (attributes) of clothes. First, a content-based image retrieval(CBIR) approach based on the bag-of-visual-words (BOW) model is developed as our baseline system, in which a codebook is constructed from extracted dominant color patches. A reranking approach is then proposed to improve search quality by exploiting clothes attributes, including the type of clothes, sleeves, patterns, etc. The experiments on photo collections show that our approach is robust to large variations of images taken in unconstrained environment, and the reranking algorithm based on attribute learning significantly improves retrieval performance in combination with the proposed baseline.